Clustering exploration for SCPCS000168

Introduction

The aim is to explore the clustering and label transfers for the sample SCPCS000168.

In order to explore the clustering results, we look into some marker genes, pathways enrichment and label transfer.

This approach would provide us a rapid idea of the quality of the clustering:

• in the case each cluster do have a set of specific marker genes, we could expect each of the cluster to have at least a different phenotype,
• if marker genes are mostly shared between clusters, we might have overclustered,
• if no marker genes are found, the quality of cells in the clusters might be impaired (high mitochondrial content? ribosomal content?)

Packages

library("Seurat")
library(sctransform)

library(SCpubr)             # for plotting 
library(tidyverse)
library(patchwork)
library(msigdbr)
library(enrichplot)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ggalluvial)
library(ggsankey)
set.seed(params$seed)

Base directories

# The base path for the OpenScPCA repository, found by its (hidden) .git directory
repository_base <- rprojroot::find_root(rprojroot::is_git_root)

# The current data directory, found within the repository base directory
data_dir <- file.path(repository_base, "data", "current", params$scpca_project_id)

# The path to this module
module_base <- file.path(repository_base, "analyses", "cell-type-wilms-tumor-06")

Input files

In this notebook, we are working with the Wilms tumor sample defined in params$sample_id from the Wilms tumor dataset param$scpca_project_id. We work with the pre-processed and labeled Seurat object that is the output of 02b_label-transfer_fetal_kidney_reference_Stewart.Rmd saved in the results directory.

result_dir <- file.path(module_base, "results", params$sample_id)

To explore the clustering results, we look into some marker genes reported here:

CellType_metadata <- read_csv(file.path(module_base, "marker-sets", "CellType_metadata.csv"))

DT::datatable(CellType_metadata, caption = ("CellType_metadata"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',
                               buttons = c( 'csv', 'excel')))

Output file

Reports will be saved in the notebook directory. The pre-processed and annotated Seurat object per samples are saved in the result folder.

Functions

Here we defined function that will be used multiple time all along the notebook.

Visualize seurat clusters and metadata

For a Seurat object objectand a metadata metadata, the function visualize_metadata will plot FeaturePlot and BarPlot

• object is the Seurat object

• metadata the gene or quantitative value to be plotted

• group.by is the metadata used for grouping the violin plots

visualize_metadata <- function(object, meta, group.by){
  
  if(is.numeric(object@meta.data[,meta])){
     d <- SCpubr::do_FeaturePlot(object, 
                                              features = meta, 
                                              pt.size = 0.2, 
                                              legend.width = 0.5, 
                                              legend.length = 5, 
                                              legend.position = "right") + ggtitle(meta)
    b <- SCpubr::do_ViolinPlot(srat, 
                                             features = meta, 
                                             ncol = 1, 
                                             group.by = group.by, 
                                             legend.position = "none")
                   
   return(d + b + plot_layout(ncol = 2, widths = c(2,4))) 
  }
  
  
  else{
    
  
  d <- SCpubr::do_DimPlot(object, reduction="umap", group.by = group.by, label = TRUE, repel = TRUE) + ggtitle(paste0(meta," - umap")) + theme(text=element_text(size=18))
  b <- SCpubr::do_BarPlot(sample = object,
                         group.by = meta,
                         split.by = group.by,
                         position = "fill",
                         font.size = 10,
                         legend.ncol = 3) +
                         ggtitle("% cells")+
                         xlab(print(group.by)) +
                         theme(text=element_text(size=18))
  return(d + b + plot_layout(ncol = 2, widths = c(2,4)))
  }
  
}

Visualize seurat clusters and markers genes

For a Seurat object objectand a features features, the function visualize_feature will plot FeaturePlot and ViolinPlot

• object is the Seurat object

• features the gene or quantitative value to be plotted

• group.by is the metadata used for grouping the violin plots

visualize_feature <- function(object, features, group.by = "seurat_clusters"){
 
                  feature_symbol <- AnnotationDbi::select(org.Hs.eg.db, 
                                keys=features, 
                                columns="SYMBOL", 
                                keytype="ENSEMBL")
 
                  d <- SCpubr::do_FeaturePlot(object, 
                                              features = feature_symbol$ENSEMBL, 
                                              pt.size = 0.2, 
                                              legend.width = 0.5, 
                                              legend.length = 5, 
                                              legend.position = "right") + ggtitle(feature_symbol$SYMBOL)
                  b <- SCpubr::do_ViolinPlot(srat, 
                                             features = feature_symbol$ENSEMBL, 
                                             ncol = 1, 
                                             group.by = group.by, 
                                             legend.position = "none",
                                             assay = "SCT") + ylab(feature_symbol$SYMBOL)
                   
                  return(d + b + plot_layout(ncol = 2, widths = c(2,4))) 
}

Calculate ModuleScore from a MSigDB dataset

For a Seurat object object, the function MSigDB_score will calculate a score using AddModuleScore() function for a MSigDB gene set gs_name in the category category

• object is the Seurat object to calculate the score. Score will be added in the metadata of this object.

• category is a MSigDB collection (https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp). values in c("H", "C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8")

• gs_name is the name of a MSigDB gene set, e.g. "HALLMARK_P53_PATHWAY"

• name is the name of the module

MSigDB_score <- function(object, category, gs_name, name){
  
        set <- msigdbr(species = "human", category = category)
        set_list <- set %>% 
                    dplyr::filter(gs_name == gs_name) %>%
                    dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()
        set_list <- list(set_list$human_ensembl_gene)
        object <- AddModuleScore(object, features = set_list, name = name)
return(object)
}

Enrichment analysis

Enrichment_plotaim to perform enrichment of the marker genes for each of the Seurat clusters and summarize the results in a dotplot.

• category is the MSigDB category or collection to be used values in c("H", "C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8")

• signatures is a list of marker genes per cluster

• backgroundis the universe used for enrichment

Enrichment_plot <- function(category, signatures, background){
    ## define genesets
   tryCatch( expr =  {
               gene_set <- msigdbr(species = "human", category = category)
               msigdbr_set <-  gene_set %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()
               cclust<-compareCluster(geneCluster = signatures, 
               fun = enricher,
               TERM2GENE = msigdbr_set,
               universe=background)
               d <- dotplot(cclust,showCategory=15) + scale_y_discrete(labels=function(x) str_wrap(x, width=40))
               return(d)  
   },
   error = function(e) { print("No enrichment") } )
   
}

do_SankeyPlot

do_SankeyPlot is largely inspired by the source code from SCpubr as the function has been removed from the package. https://github.com/enblacar/SCpubr/blob/v1.1.2-dev-stable/R/do_SankeyPlot.R

• first_group is a metadata variable. First group of nodes of the sankey plot.

• last_group is a metadata variable. Last group of nodes of the sankey plot.

• middle_groups is a metadata variable. Vector of groups of nodes of the sankey plot.

• type is a type of plot to make. One of:

  • sankey: Generates a sankey plot.
  • alluvial: Generated an Alluvial plot, a kind of sankey plot where all groups have the same height.

• width Width of the nodes.

• space Vertical space between the nodes. It appears to be equal to a single cell. Use big numbers to see a difference (like, 1000 or 10000).

• position GGplot2 position.

• node.fill Color to fill the nodes.

• node.color Color for the contour of the nodes.

• flow.alpha Alpha of the connections.

• flow.color Color for the contour of the connections.

• text_size Size of the labels.

• text_color Color of the labels.

• smooth How smooth the connections are.

• use_labels Whether to use labels or text for the node names.

• hjust General hjust for the labels.

do_SankeyPlot <- function(sample,
                          first_group,
                          last_group,
                          type = "sankey",
                          middle_groups = NULL,
                          width = 0.1,
                          space = ifelse(type == "sankey", 0.05 * ncol(sample), 0),
                          position = "identity",
                          node.fill = "white",
                          node.color = "white",
                          flow.alpha = 0.75,
                          flow.color = "black",
                          text_size = 3,
                          text_color = "black",
                          font.size = 18,
                          font.type = "sans",
                          smooth = 8,
                          use_labels = FALSE,
                          hjust = NULL,
                          colors.first = NULL,
                          colors.middle = NULL,
                          colors.last = NULL,
                          plot.title = NULL,
                          plot.subtitle = NULL,
                          plot.caption = NULL){

 

  # Check logical parameters.
  logical_list <- list("use_labels" = use_labels)
  # Check numeric parameters.
  numeric_list <- list("width" = width,
                       "space" = space,
                       "flow.alpha" = flow.alpha,
                       "text_size" = text_size,
                       "font.size" = font.size,
                       "smooth" = smooth,
                       "hjust" = hjust)
  # Check character parameters.
  character_list <- list("first_group" = first_group,
                         "last_group" = last_group,
                         "middle_groups" = middle_groups,
                         "type" = type,
                         "position" = position,
                         "node.color" = node.color,
                         "flow.color" = flow.color,
                         "text_color" = text_color,
                         "font.type" = font.type,
                         "colors.first" = colors.first,
                         "colors.middle" = colors.middle,
                         "colors.last" = colors.last,
                         "node.fill" = node.fill,
                         "plot.title" = plot.title,
                         "plot.subtitle" = plot.subtitle,
                         "plot.caption" = plot.caption)

   

  `%>%` <- magrittr::`%>%`

  data <- suppressWarnings({sample@meta.data %>%
                            dplyr::select(dplyr::all_of(c(first_group, middle_groups, last_group))) %>%
                            tibble::rownames_to_column(var = "cell") %>%
                            dplyr::select(-.data$cell) %>%
                            ggsankey::make_long(dplyr::all_of(c(first_group, middle_groups, last_group))) %>%
                            dplyr::rowwise() %>%
                            dplyr::mutate(hjust = if(.data$x %in% middle_groups){0.5}
                                          else if (.data$x == last_group){0}
                                          else if (.data$x == first_group){1})})
  if (!is.null(hjust)){data$hjust <- hjust}

  
  
  colors.first <- SCpubr::do_ColorPalette(colors.use = "steelblue",
                        n = length(unique(sample@meta.data[, first_group])))
    if (is.factor(sample@meta.data[, first_group])){
      names(colors.first) <- levels(sample@meta.data[, first_group])
    } else {
      names(colors.first) <- sort(unique(sample@meta.data[, first_group]))
    }
  

  colors.last <- SCpubr::do_ColorPalette(colors.use = "steelblue",
                        n = length(unique(sample@meta.data[, last_group])))
    if (is.factor(sample@meta.data[, last_group])){
      names(colors.last) <- levels(sample@meta.data[, last_group])
    } else {
      names(colors.last) <- sort(unique(sample@meta.data[, last_group]))
    }
  


  colors.use <- c(colors.first, colors.last)
  func_use <- ifelse(isTRUE(use_labels), ggsankey::geom_sankey_label, ggsankey::geom_sankey_text)

  p <- data %>%

       ggplot2::ggplot(mapping = ggplot2::aes(x = .data$x,
                                              next_x = .data$next_x,
                                              node = .data$node,
                                              next_node = .data$next_node,
                                              fill = factor(.data$node),
                                              label = .data$node,
                                              hjust = .data$hjust)) +
       ggsankey::geom_sankey(flow.alpha = flow.alpha,
                             node.color = node.color,
                             node.fill = node.fill,
                             color = flow.color,
                             width = width,
                             position = position,
                             type = type,
                             space = space) +
       func_use(size = text_size,
                color = text_color,
                fontface = "bold",
                position = position,
                type = type,
                space = space) +
       ggplot2::scale_fill_manual(values = colors.use) +
       ggplot2::xlab("") +
       ggplot2::ylab("") +
       ggplot2::labs(title = plot.title,
                     subtitle = plot.subtitle,
                     caption = plot.caption) +
       ggplot2::theme_minimal(base_size = font.size) +
       ggplot2::theme(axis.title = ggplot2::element_text(color = "black",
                                                         face = "bold"),
                      axis.line.x = ggplot2::element_blank(),
                      axis.text.x = ggplot2::element_text(color = "black",
                                                          face = "bold",
                                                          angle = 0,
                                                          hjust = 0.5,
                                                          vjust = 1),
                      axis.text.x.top = ggplot2::element_text(color = "black",
                                                              face = "bold",
                                                              angle = 0,
                                                              hjust = 0.5,
                                                              vjust = 1),
                      axis.text.y = ggplot2::element_blank(),
                      axis.ticks = ggplot2::element_blank(),
                      panel.grid.major = ggplot2::element_blank(),
                      plot.title.position = "plot",
                      plot.title = ggplot2::element_text(face = "bold", hjust = 0),
                      plot.subtitle = ggplot2::element_text(hjust = 0),
                      plot.caption = ggplot2::element_text(hjust = 1),
                      panel.grid = ggplot2::element_blank(),
                      text = ggplot2::element_text(family = font.type),
                      plot.caption.position = "plot",
                      legend.text = ggplot2::element_text(face = "bold"),
                      legend.position = "none",
                      legend.title = ggplot2::element_text(face = "bold"),
                      legend.justification = "center",
                      plot.margin = ggplot2::margin(t = 10, r = 10, b = 10, l = 10),
                      plot.background = ggplot2::element_rect(fill = "white", color = "white"),
                      panel.background = ggplot2::element_rect(fill = "white", color = "white"),
                      legend.background = ggplot2::element_rect(fill = "white", color = "white"),
                      strip.text =ggplot2::element_text(color = "black", face = "bold"))

  return(p)
}

do_Table_Heatmap

do_Table_Heatmap shows heatmap of counts of cells in groups of metadata

• data seurat object
• first_group is the name of the first metadata to group the cells
• last_group is the name of the second metadata to group the cells

do_Table_Heatmap <- function(data, first_group, last_group ){
  
  df <- data@meta.data %>%
  mutate_if(sapply(data@meta.data, is.character), as.factor)  %>% 
  group_by( !!sym(first_group), !!sym(last_group))%>% 
  summarise(Nb = n()) 

p <- ggplot(df, aes(x= !!sym(first_group), y = !!sym(last_group), fill = Nb)) +
   geom_tile() +
   scale_fill_viridis(discrete=FALSE)  +
   theme_bw() +
   theme(text = element_text(size = 20))

return(p)
}

Analysis

Load the pre-processed Seurat object

# open the processed rds object
srat <- readRDS(file.path(result_dir, paste0("02b-fetal_kidney_label-transfer_",params$sample_id,".Rds")))
DefaultAssay(srat) <- "SCT"

Visualize seurat clusters

We expect up to 5 set of clusters:

• blastema cancer cells
• epithelium cancer and/or normal cells
• stroma cancer and/or normal cells
• immune cells
• endothelial cells

d2 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - umap")
d1 <- SCpubr::do_DimPlot(srat, reduction="pca", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - pca")
v1 <- SCpubr::do_ViolinPlot(srat, features = c( "subsets_mito_percent"), ncol = 1, group.by = "seurat_clusters", legend.position = "none") 
v2 <- SCpubr::do_ViolinPlot(srat, features = c( "detected"), ncol = 1, group.by = "seurat_clusters", legend.position = "none") 
v3 <- SCpubr::do_ViolinPlot(srat, features = c( "sum"), ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + (v1 + v2 +v3 + plot_layout(ncol=1) ) + plot_layout(ncol = 3, widths = c(2,2,4))

Cell cycle information

s.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$s.genes]
g2m.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$g2m.genes]
srat <- CellCycleScoring(srat, s.features = s.genes, g2m.features = g2m.genes, set.ident = FALSE)

visualize_metadata(srat, meta = "Phase", group.by = "seurat_clusters")

## [1] "seurat_clusters"

visualize_metadata(srat, meta  = "S.Score", group.by = "seurat_clusters")

visualize_metadata(srat, meta = "G2M.Score", group.by = "seurat_clusters")

Look at specific genes

Here, we open the table of marker genes marker-sets/CellType_metadata.csv. Note: we do not expect to have a clear and nice pattern of expression for all of the following markers in every tumor. This is just ti get a few idea.

for(feature in CellType_metadata$ENSEMBL_ID[CellType_metadata$ENSEMBL_ID %in% rownames(srat@assays$SCT)]){
   print(visualize_feature(srat, features = feature, group.by = "seurat_clusters"))
}

Look at specific pathways

TP53 pathway

here we will calculate a TP53 score using AddMduleScore and the genes of the HALLMARK_P53_PATHWAY gene set.

srat <- MSigDB_score(object = srat , category = "H", gs_name = "HALLMARK_P53_PATHWAY", name = "TP53_score")
visualize_metadata(srat, meta = "TP53_score1", group.by = "seurat_clusters" )

DNA repair pathway

here we will calculate a DNA_repair score using AddMduleScore and the genes of the HALLMARK_DNA_REPAIR gene set.

srat <- MSigDB_score(object = srat , category = "H", gs_name = "HALLMARK_DNA_REPAIR", name = "DNA_repair_score")
visualize_metadata(srat, meta = "DNA_repair_score1", group.by = "seurat_clusters" )

Note: Chemo-treated samples should have higher DNA-damage scores.

DROSHA target genes

srat <- MSigDB_score(object = srat , category = "C3", gs_name = "DROSHA_TARGET_GENES", name = "DROSHA_score")
visualize_metadata(srat, meta = "DROSHA_score1", group.by = "seurat_clusters" )

DICER1 target genes

srat <- MSigDB_score(object = srat , category = "C3", gs_name = "DICER1_TARGET_GENES", name = "DICER1_score")
visualize_metadata(srat, meta = "DICER1_score1", group.by = "seurat_clusters" )

Find marker genes for each of the seurat clusters

In addition to the list of known marker genes, we used an unbiased approach to find transcripts that characterized the different clusters.

We run DElegate::FindAllMarkers2 to find markers of the different clusters and manually check if they do make sense. DElegate::FindAllMarkers2 is an improved version of Seurat::FindAllMarkers based on pseudobulk differential expression method. Please check the preprint from Chistoph Hafemeister: https://www.biorxiv.org/content/10.1101/2023.03.28.534443v1 and tool described here: https://github.com/cancerbits/DElegate

Of note, we won’t use it for annotation, this is just here to get an idea!

feature_conversion <- srat@assays$RNA@meta.data
de_results   <- DElegate::FindAllMarkers2(srat, group_column = "seurat_clusters")
#filter the most relevant markers
s.markers <- de_results[de_results$padj < params$padj_thershold & de_results$log_fc > params$lfc_threshold & de_results$rate1 > params$rate1_threshold,]
# add gene symbol for easiest interpretation of the result
s.markers$gene_ids <- s.markers$feature
s.markers <- left_join(s.markers,feature_conversion, by = c( "gene_ids") )
stopifnot(
  "Error joining gene ids and feature names" = identical(s.markers$feature, s.markers$gene_ids)   
)

DT::datatable(s.markers, caption = ("marker genes"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',
                               buttons = c( 'csv', 'excel')))

# Select top 5 genes for heatmap plotting
s.markers <- na.omit(s.markers)
s.markers %>%
    group_by(group1) %>%
    top_n(n =  5, wt = log_fc) -> top5
# subset for plotting
cells <- WhichCells(srat, downsample = 100)
ss <- subset(srat, cells = cells)
ss <- ScaleData(ss, features = top5$feature)
p1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
p2 <- DoHeatmap(ss, features = top5$feature,  cells = cells, group.by = "seurat_clusters") + NoLegend() + 
  scale_fill_gradientn(colors =  c("#01665e","#35978f",'darkslategray3', "#f7f7f7", "#fee391","#fec44f","#F9AD03")) 
p3 <- ggplot(srat@meta.data, aes(seurat_clusters, fill = seurat_clusters)) + geom_bar() + NoLegend()
common_title <- sprintf("Unsupervised clustering %s, %d cells", srat@meta.data$orig.ident[1], ncol(srat))
show((((p1 / p3) + plot_layout(heights = c(3,2)) | p2) ) + plot_layout(widths = c(1, 2)) + plot_layout(heights = c(3,1)) + plot_annotation(title = common_title))

DT::datatable(top5[, c(1, 9, 11, 12)], caption = ("top 5 marker genes"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',
                               buttons = c( 'csv', 'excel')))

Enrichment analysis of marker genes

Here we perform enrichment analysis of the marker genes found in the previous section for each Seurat cluster.

We defined as universe/background all the genes expressed in the dataset, meaning the rownames of the Seurat object.

We used three gene sets from MSiGDB:

• the hallmark gene sets are coherently expressed signatures derived by aggregating many MSigDB gene sets to represent well-defined biological states or processes.
• the C3 : regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites.
• the C8 : cell type signature gene sets curated from cluster markers identified in single-cell sequencing studies of human tissue.

We used enricher function from clusterProfiler to perform enrichment analysis.

# define background genes = universe for enrichment
background <-  rownames(srat)
# Define gene signature per cluster
signatures <- list()
for( i in unique(s.markers$group1)){
  signatures[[paste0("cluster ",i)]] <- s.markers$feature[s.markers$group1 == i]
}

Hallmarks MSiGDB gene sets

Enrichment_plot(category = "H", signatures = signatures, background = background)

EMT signature should be enriched in stroma cluster. E2F/proliferation should be enriched in blastema cluster. MYC(N), TP53 must be enriched in blastema cluster.

C8 MSiGDB cell type signature gene sets

Enrichment_plot(category = "C8", signatures = signatures, background = background)

The MSigDB C8 gene set is quite relevant for kidney and nephroblastoma annotations. Epithelial (cancer and normal) cells should be enriched in mature/adulte kidney pathways while blastema cancer cells will show enrichment of fetal kidney development pathway / cap mesenchyme.

Cell annotation

Annotation from SingleR (no cancer or kidney specific dataset)

Here, we quickly checked annotations that are present in the _processed rds object. However, the automated annotation have not been performed using a cancer specific reference or a kidney reference. We do not expect a nice labelling of the cells as the overlap of cell types between the reference and the query dataset is poor. This support the need to do a proper label transfer from the fetal kidney atlas, which is imho the best reference that can be applied to a Wilms tumor query.

visualize_metadata(srat, meta = "singler_celltype_annotation", group.by = "seurat_clusters")

## [1] "seurat_clusters"

Annotation from cellassign (no cancer or kidney specific dataset)

visualize_metadata(srat, meta = "cellassign_celltype_annotation", group.by = "seurat_clusters")

## [1] "seurat_clusters"

Annotation from the fetal reference (Cao et al)

visualize_metadata(srat, meta = "fetal_full_predicted.organ", group.by = "seurat_clusters")

## [1] "seurat_clusters"

visualize_metadata(srat, meta = "fetal_full_predicted.annotation.l1", group.by = "seurat_clusters")

## [1] "seurat_clusters"

visualize_metadata(srat, meta = "fetal_full_predicted.annotation.l2", group.by = "seurat_clusters")

## [1] "seurat_clusters"

Annotation from the kidney reference (Stewart et al)

visualize_metadata(srat, meta = "fetal_kidney_predicted.compartment", group.by = "seurat_clusters")

## [1] "seurat_clusters"

visualize_metadata(srat, meta = "fetal_kidney_predicted.cell_type", group.by = "seurat_clusters")

## [1] "seurat_clusters"

Compare annotations with marker genes

markers = list("malignant" = CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class == "malignant"],
            "immune"= CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class == "immune"],
            "endothelium"= CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class == "endothelium"],
            "non-malignant"= CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class == "non-malignant"])

p1 <- SCpubr::do_DotPlot(sample = srat,
                         group.by =  "fetal_kidney_predicted.compartment",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE, 
                         plot.title = "fetal_kidney_predicted.compartment"
                         ) 

## Warning: 
## ! The following features were not be found:NA, ENSG00000156738, NA, NA, NA.

p2 <- SCpubr::do_DotPlot(sample = srat,
                         group.by =  "fetal_full_predicted.annotation.l1",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE,
                         plot.title = "fetal_full_predicted.annotation.l1"
                          )

## Warning: 
## ! The following features were not be found:NA, ENSG00000156738, NA, NA, NA.

p3 <- SCpubr::do_DotPlot(sample = srat,
                         group.by =   "seurat_clusters",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE,
                         plot.title =  "seurat_clusters"
                          )

## Warning: 
## ! The following features were not be found:NA, ENSG00000156738, NA, NA, NA.

p4 <- SCpubr::do_DotPlot(sample = srat,
                         group.by =   "cellassign_celltype_annotation",
                         features = markers, 
                         axis.text.x.angle = 90, 
                         flip = TRUE,
                         plot.title = "cellassign_celltype_annotation"
                          )

## Warning: 
## ! The following features were not be found:NA, ENSG00000156738, NA, NA, NA.

(p1|p2)/(p3|p4)

Compare annotations between them

Sankey plots

p1 <- do_SankeyPlot(sample = srat,
                            first_group = "fetal_kidney_predicted.compartment",
                            last_group = "seurat_clusters",
                             type = "sankey",text_size = 5, use_labels = TRUE
                            )


p2 <- do_SankeyPlot(sample = srat,
                            first_group = "fetal_full_predicted.annotation.l1",
                            last_group = "seurat_clusters",
                             type = "sankey",text_size = 5, use_labels = TRUE
                            )


p3 <- do_SankeyPlot(sample = srat,
                            first_group = "singler_celltype_annotation",
                            last_group = "seurat_clusters",
                             type = "sankey",text_size = 4, use_labels = TRUE
                            )


p4 <- do_SankeyPlot(sample = srat,
                            first_group = "cellassign_celltype_annotation",
                            last_group = "seurat_clusters",
                             type = "sankey",text_size = 5, use_labels = TRUE
                            )

(p1|p2)/(p3|p4)

## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_label()`).

p1 <- do_SankeyPlot(sample = srat,
                            first_group = "fetal_kidney_predicted.compartment",
                            last_group = "fetal_full_predicted.annotation.l1",
                            type = "sankey",text_size = 6, use_labels = TRUE
 )

p2 <- do_SankeyPlot(sample = srat,
                            first_group = "fetal_kidney_predicted.compartment",
                            last_group = "fetal_kidney_predicted.cell_type",
                            type = "sankey",text_size = 6, use_labels = TRUE                                                        )

p1 | p2

#### Heatmaps

p1 <- do_Table_Heatmap( srat,
                            last_group = "fetal_kidney_predicted.compartment",
                            first_group = "seurat_clusters"
                             
                            )


p2 <- do_Table_Heatmap( srat,
                            last_group = "fetal_full_predicted.annotation.l1",
                            first_group = "seurat_clusters"
                            )


p3 <- do_Table_Heatmap( srat,
                            last_group = "singler_celltype_annotation",
                            first_group = "seurat_clusters"
                            )


p4 <- do_Table_Heatmap( srat,
                            last_group = "cellassign_celltype_annotation",
                            first_group = "seurat_clusters"
                            )

(p1|p2)/(p3|p4)

Session Info

# record the versions of the packages used in this analysis and other environment information
sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Vienna
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] viridis_0.6.5               viridisLite_0.4.2           ggsankey_0.0.99999          pkgload_1.4.0               ggalluvial_0.12.5           org.Hs.eg.db_3.19.1        
##  [7] AnnotationDbi_1.66.0        clusterProfiler_4.12.6      enrichplot_1.24.4           msigdbr_7.5.1               sctransform_0.4.1           SeuratData_0.2.2.9001      
## [13] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.1        GenomeInfoDb_1.40.1         IRanges_2.38.1             
## [19] S4Vectors_0.42.1            BiocGenerics_0.50.0         MatrixGenerics_1.16.0       matrixStats_1.3.0           patchwork_1.2.0             lubridate_1.9.3            
## [25] forcats_1.0.0               stringr_1.5.1               dplyr_1.1.4                 purrr_1.0.2                 readr_2.1.5                 tidyr_1.3.1                
## [31] tibble_3.2.1                ggplot2_3.5.1               tidyverse_2.0.0             SCpubr_2.0.2                Azimuth_0.5.0               shinyBS_0.61.1             
## [37] Seurat_5.1.0                SeuratObject_5.0.2          sp_2.1-4                    optparse_1.7.5             
## 
## loaded via a namespace (and not attached):
##   [1] R.methodsS3_1.8.2                 vroom_1.6.5                       urlchecker_1.0.1                  poweRlaw_0.80.0                   goftest_1.2-3                    
##   [6] DT_0.33                           Biostrings_2.72.1                 vctrs_0.6.5                       spatstat.random_3.3-1             digest_0.6.37                    
##  [11] png_0.1-8                         ggrepel_0.9.5                     deldir_2.0-4                      parallelly_1.38.0                 renv_1.0.7                       
##  [16] MASS_7.3-61                       Signac_1.14.0                     reshape2_1.4.4                    httpuv_1.6.15                     qvalue_2.36.0                    
##  [21] withr_3.0.1                       xfun_0.47                         ggfun_0.1.6                       ellipsis_0.3.2                    survival_3.7-0                   
##  [26] EnsDb.Hsapiens.v86_2.99.0         memoise_2.0.1                     gson_0.1.0                        profvis_0.3.8                     DElegate_1.2.1                   
##  [31] tidytree_0.4.6                    zoo_1.8-12                        gtools_3.9.5                      pbapply_1.7-2                     R.oo_1.26.0                      
##  [36] KEGGREST_1.44.1                   promises_1.3.0                    httr_1.4.7                        restfulr_0.0.15                   globals_0.16.3                   
##  [41] fitdistrplus_1.2-1                rhdf5filters_1.16.0               ps_1.7.7                          rhdf5_2.48.0                      rstudioapi_0.16.0                
##  [46] UCSC.utils_1.0.0                  miniUI_0.1.1.1                    generics_0.1.3                    DOSE_3.30.5                       processx_3.8.4                   
##  [51] babelgene_22.9                    curl_5.2.2                        zlibbioc_1.50.0                   ggraph_2.2.1                      polyclip_1.10-7                  
##  [56] GenomeInfoDbData_1.2.12           SparseArray_1.4.8                 desc_1.4.3                        xtable_1.8-4                      pracma_2.4.4                     
##  [61] evaluate_0.24.0                   S4Arrays_1.4.1                    hms_1.1.3                         irlba_2.3.5.1                     colorspace_2.1-1                 
##  [66] hdf5r_1.3.11                      getopt_1.20.4                     ROCR_1.0-11                       reticulate_1.38.0                 spatstat.data_3.1-2              
##  [71] magrittr_2.0.3                    lmtest_0.9-40                     later_1.3.2                       ggtree_3.12.0                     lattice_0.22-6                   
##  [76] spatstat.geom_3.3-2               future.apply_1.11.2               shadowtext_0.1.4                  scattermore_1.2                   XML_3.99-0.17                    
##  [81] cowplot_1.1.3                     RcppAnnoy_0.0.22                  pillar_1.9.0                      nlme_3.1-166                      pwalign_1.0.0                    
##  [86] caTools_1.18.2                    compiler_4.4.1                    RSpectra_0.16-2                   stringi_1.8.4                     devtools_2.4.5                   
##  [91] tensor_1.5                        GenomicAlignments_1.40.0          plyr_1.8.9                        crayon_1.5.3                      abind_1.4-5                      
##  [96] BiocIO_1.14.0                     gridGraphics_0.5-1                googledrive_2.1.1                 locfit_1.5-9.10                   graphlayouts_1.1.1               
## [101] bit_4.0.5                         fastmatch_1.1-4                   codetools_0.2-20                  crosstalk_1.2.1                   bslib_0.8.0                      
## [106] plotly_4.10.4                     mime_0.12                         splines_4.4.1                     Rcpp_1.0.13                       fastDummies_1.7.4                
## [111] sparseMatrixStats_1.16.0          cellranger_1.1.0                  knitr_1.48                        blob_1.2.4                        utf8_1.2.4                       
## [116] seqLogo_1.70.0                    AnnotationFilter_1.28.0           fs_1.6.4                          listenv_0.9.1                     pkgbuild_1.4.4                   
## [121] ggplotify_0.1.2                   Matrix_1.7-0                      callr_3.7.6                       statmod_1.5.0                     tzdb_0.4.0                       
## [126] tweenr_2.0.3                      pkgconfig_2.0.3                   tools_4.4.1                       cachem_1.1.0                      RSQLite_2.3.7                    
## [131] DBI_1.2.3                         fastmap_1.2.0                     rmarkdown_2.28                    scales_1.3.0                      grid_4.4.1                       
## [136] usethis_2.2.3                     ica_1.0-3                         shinydashboard_0.7.2              Rsamtools_2.20.0                  sass_0.4.9                       
## [141] dotCall64_1.1-1                   RANN_2.6.2                        farver_2.1.2                      scatterpie_0.2.4                  tidygraph_1.3.1                  
## [146] yaml_2.3.10                       rtracklayer_1.64.0                cli_3.6.3                         leiden_0.4.3.1                    lifecycle_1.0.4                  
## [151] uwot_0.2.2                        sessioninfo_1.2.2                 presto_1.0.0                      BSgenome.Hsapiens.UCSC.hg38_1.4.5 BiocParallel_1.38.0              
## [156] annotate_1.82.0                   timechange_0.3.0                  gtable_0.3.5                      rjson_0.2.22                      ggridges_0.5.6                   
## [161] progressr_0.14.0                  limma_3.60.4                      parallel_4.4.1                    ape_5.8                           edgeR_4.2.1                      
## [166] jsonlite_1.8.8                    RcppHNSW_0.6.0                    TFBSTools_1.42.0                  bitops_1.0-8                      assertthat_0.2.1                 
## [171] bit64_4.0.5                       Rtsne_0.17                        yulab.utils_0.1.7                 spatstat.utils_3.1-0              CNEr_1.40.0                      
## [176] highr_0.11                        jquerylib_0.1.4                   GOSemSim_2.30.2                   shinyjs_2.1.0                     SeuratDisk_0.0.0.9021            
## [181] spatstat.univar_3.0-0             R.utils_2.12.3                    lazyeval_0.2.2                    shiny_1.9.1                       htmltools_0.5.8.1                
## [186] GO.db_3.19.1                      rappdirs_0.3.3                    ensembldb_2.28.1                  glue_1.7.0                        TFMPvalue_0.0.9                  
## [191] spam_2.10-0                       googlesheets4_1.1.1               httr2_1.0.3                       XVector_0.44.0                    RCurl_1.98-1.16                  
## [196] rprojroot_2.0.4                   treeio_1.28.0                     BSgenome_1.72.0                   gridExtra_2.3                     JASPAR2020_0.99.10               
## [201] igraph_2.0.3                      R6_2.5.1                          labeling_0.4.3                    RcppRoll_0.3.1                    GenomicFeatures_1.56.0           
## [206] cluster_2.1.6                     Rhdf5lib_1.26.0                   gargle_1.5.2                      aplot_0.2.3                       DirichletMultinomial_1.46.0      
## [211] DelayedArray_0.30.1               tidyselect_1.2.1                  ProtGenerics_1.36.0               ggforce_0.4.2                     future_1.34.0                    
## [216] munsell_0.5.1                     KernSmooth_2.23-24                data.table_1.16.0                 htmlwidgets_1.6.4                 fgsea_1.30.0                     
## [221] RColorBrewer_1.1-3                rlang_1.1.4                       spatstat.sparse_3.1-0             spatstat.explore_3.3-2            remotes_2.5.0                    
## [226] fansi_1.0.6